Probabilistic Models for Handwritten Text

As discussed in the previous chapter, PrIx (and KWS), can be fruitfully seen under a handwritten text recognition (HTR) viewpoint. So, this chapter reviews segmentation-free approaches to HTR and the corresponding probabilistic models. Pre-processing steps used in traditional HTR workflows, such as handwriting style normalization and hand-crafted feature extraction, are briefly outlined. More recent techniques like Convolutional Neural Networks for feature extraction learning, and neural-network-based text line detection, are reviewed in greater detail. Probabilistic “optical models” for HTR are characterized by modeling simultaneously textual contents and geometric positions (alignment) of handwritten strokes on images. We review Recurrent Neural Networks (RNN) and, in particular, the Long-Short Term Memory RNN model, along with the, so called, Connectionist Temporal Classification loss function, which are currently considered state of the art.We also review the time-honored Hidden Markov Models, not only because of their sound formalization and paradigmatic role in the in the field of HTR, but also because they are still useful nowadays as a convenient asset to combine all types of optical models with linguistic constraints related with lexicon and syntax. These constraints have traditionally been modeled with #-grams, which can bust HTR (and PrIx) performance, sometimes very significantly. #-grams are also thoroughly reviewed, including the way they can seamlessly be integrated with HMMs or RNNs using Weighted Finite State Transducers and the corresponding automata algebra. Finally, Word Graph (or lattice) concepts and methods are explained in full detail since, as it was foretold in the previous chapter, these graphs constitute a key tool for PrIx computation.